tencent/HY-Embodied-0.5 · Hugging Face

Source: https://huggingface.co/tencent/HY-Embodied-0.5 A Family of Embodied Foundation Models for Real-World Agents

Tencent Robotics X × HY Vision Team

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%94%A5-updates🔥 Updates

• \[2026\-04\-09\]🚀 We have releasedHY-Embodied-0.5, featuring the open-sourcedHY\-Embodied\-0\.5 MoT\-2Bweights onHugging Facealong with the official inference code!

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%93%96-abstract📖 Abstract

We introduceHY-Embodied-0.5, a suite of foundation models tailored specifically for real-world embodied intelligence. To bridge the gap between general Vision-Language Models (VLMs) and the strict demands of physical agents, our models are engineered to excel in spatial-temporal visual perception and complex embodied reasoning (prediction, interaction, and planning).

The suite features an innovativeMixture-of-Transformers (MoT)architecture utilizing latent tokens for modality-specific computing, significantly enhancing fine-grained perception. It includes two primary variants: a highly efficient2B modelfor edge deployment and a powerful32B modelfor complex reasoning. Through a self-evolving post-training paradigm and large-to-small on-policy distillation, our compact MoT-2B outperforms state-of-the-art models of similar size across 16 benchmarks, while the 32B variant achieves frontier-level performance comparable to Gemini 3.0 Pro. Ultimately, HY-Embodied serves as a robust “brain” for Vision-Language-Action (VLA) pipelines, delivering compelling results in real-world physical robot control.

https://huggingface.co/tencent/HY-Embodied-0.5#%E2%AD%90%EF%B8%8F-key-features⭐️ Key Features

• 🧠Evolved MoT Architecture:Designed for maximum efficiency without sacrificing visual acuity. The MoT-2B variant contains 4B total parameters but requiresonly 2.2B activated parametersduring inference. By emphasizing modality-specific computing in the vision pathway, it achieves the high inference speed of a dense 2B model while delivering superior, fine-grained perceptual representations.
• 🔗**High-Quality Mixed Chain Reasoning:**We introduce an advanced iterative, self-evolving post-training pipeline. By employing on-policy distillation, we successfully transfer the sophisticated step-by-step reasoning, planning, and high-quality “thinking” capabilities from our powerful 32B model directly to the compact 2B variant.
• 🌍Large-Scale Embodied Pre-training:Grounded in a massive, specially curated dataset comprising>100 millionembodied and spatial-specific data points. Trained on a corpus exceeding200 billion tokens, the model develops a deep, native understanding of 3D spaces, physical object interactions, and agent dynamics.
• 🦾**Stronger VLA Application:**Beyond standard academic benchmarks, HY-Embodied is engineered to be the core cognitive engine for physical robots. It seamlessly integrates into Vision-Language-Action (VLA) frameworks, acting as a highly robust and capable brain to drive high success rates in complex, real-world robotic control tasks.

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%93%85-plannings📅 Plannings

• Transformers Inference
• vLLM Inference
• Fine-tuning Code
• Online Gradio Demo

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%9B%A0%EF%B8%8F-dependencies-and-installation🛠️ Dependencies and Installation

https://huggingface.co/tencent/HY-Embodied-0.5#prerequisitesPrerequisites

• 🖥️Operating System: Linux (recommended)
• 🐍Python: 3.12+ (recommended and tested)
• ⚡CUDA: 12.6
• 🔥PyTorch: 2.8.0
• 🎮GPU: NVIDIA GPU with CUDA support

https://huggingface.co/tencent/HY-Embodied-0.5#installationInstallation

1. Install the specific Transformers version required for this model:

pip install git+https://github.com/huggingface/transformers@9293856c419762ebf98fbe2bd9440f9ce7069f1a

Note: We will merge the improvements into the Transformers main branch later.

1. Install other dependencies:

pip install -r requirements.txt

https://huggingface.co/tencent/HY-Embodied-0.5#quick-startQuick Start

1. Clone the repository:

git clone https://github.com/Tencent-Hunyuan/HY-Embodied
cd HY-Embodied/

1. Install dependencies:

pip install -r requirements.txt

1. Run inference:

python inference.py

The example script demonstrates both single generation and batch generation capabilities.

https://huggingface.co/tencent/HY-Embodied-0.5#model-downloadModel Download

The code automatically downloads the modeltencent/HY\-Embodied\-0\.5from Hugging Face Hub. Ensure you have sufficient disk space (8 GB) for the model weights.

https://huggingface.co/tencent/HY-Embodied-0.5#hardware-requirementsHardware Requirements

• GPU: Recommended for optimal performance (NVIDIA GPU with at least 16GB VRAM)
• CPU: Supported but slower
• Memory: At least 16GB RAM recommended
• Storage: 20GB+ free space for model and dependencies

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%9A%80-quick-start-with-transformers🚀 Quick Start with Transformers

https://huggingface.co/tencent/HY-Embodied-0.5#basic-inference-exampleBasic Inference Example

import os
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

# Load model & processor
MODEL_PATH = "tencent/HY-Embodied-0.5"
DEVICE = "cuda"
THINKING_MODE = False
TEMPERATURE = 0.8

processor = AutoProcessor.from_pretrained(MODEL_PATH)

# Load chat template if available
chat_template_path = os.path.join(MODEL_PATH, "chat_template.jinja")
if os.path.exists(chat_template_path):
    processor.chat_template = open(chat_template_path).read()

model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16)
model.to(DEVICE).eval()

# Prepare input messages
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "./figures/example.jpg"},
            {"type": "text", "text": "Describe the image in detail."},
        ],
    }
]

# Process and generate
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
    enable_thinking=THINKING_MODE,
).to(model.device)

with torch.no_grad():
    generated_ids = model.generate(
        **inputs,
        max_new_tokens=32768,
        use_cache=True,
        temperature=TEMPERATURE,
        do_sample=TEMPERATURE > 0,
    )

output_ids = [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)]
print(processor.batch_decode(output_ids, skip_special_tokens=True)[0])

https://huggingface.co/tencent/HY-Embodied-0.5#batch-inferenceBatch Inference

import os
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

# Load model & processor
MODEL_PATH = "tencent/HY-Embodied-0.5"
DEVICE = "cuda"
THINKING_MODE = False
TEMPERATURE = 0.8

processor = AutoProcessor.from_pretrained(MODEL_PATH)

# Load chat template if available
chat_template_path = os.path.join(MODEL_PATH, "chat_template.jinja")
if os.path.exists(chat_template_path):
    processor.chat_template = open(chat_template_path).read()

model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16)
model.to(DEVICE).eval()

# Batch Inference (multiple prompts at once)
messages_batch = [
    # Sample A: image + text
    [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": "./figures/example.jpg"},
                {"type": "text", "text": "Describe the image in detail."},
            ],
        }
    ],
    # Sample B: text only
    [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "How to open a fridge?"},
            ],
        }
    ],
]

# Process each message independently
all_inputs = []
for msgs in messages_batch:
    inp = processor.apply_chat_template(
        msgs,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt",
        enable_thinking=THINKING_MODE,
    )
    all_inputs.append(inp)

# Left-pad and batch
batch = processor.pad(all_inputs, padding=True, padding_side="left").to(model.device)

with torch.no_grad():
    batch_generated_ids = model.generate(
        **batch,
        max_new_tokens=32768,
        use_cache=True,
        temperature=TEMPERATURE,
        do_sample=TEMPERATURE > 0,
    )

# Decode: strip the padded input portion
padded_input_len = batch["input_ids"].shape[1]
for i, msgs in enumerate(messages_batch):
    out_ids = batch_generated_ids[i][padded_input_len:]
    print(f"\n--- Sample {i} ---")
    print(processor.decode(out_ids, skip_special_tokens=True))

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%93%8A-evaluation📊 Evaluation

https://huggingface.co/tencent/HY-Embodied-0.5#visual-perceptionVisual Perception

Note: We evaluated HY-Embodied-0.5 MoT-2B across 22 embodied-relevant benchmarks against models of similar size. For detailed performance metrics and methodology, please refer to our technical report.

Note: We observed that small models from the Qwen3.5 series produce repetitive thinking patterns in some benchmarks, which leads to lower overall results. Therefore, we compare against Qwen3-VL models in our evaluations.

BenchmarkHY-Embodied 0.5 MoT-2BQwen3-VL 2BQwen3-VL 4BRoboBrain 2.5 4BMiMo-Embodied 7BCV-Bench89.280.085.786.988.8DA-2K92.369.576.579.472.2

https://huggingface.co/tencent/HY-Embodied-0.5#embodied-understandingEmbodied Understanding

BenchmarkHY-Embodied 0.5 MoT-2BQwen3-VL 2BQwen3-VL 4BRoboBrain 2.5 4BMiMo-Embodied 7BERQA54.541.847.343.346.8EmbSpatial-Bench82.875.980.773.876.2RoboBench-MCQ49.236.945.844.443.6RoboBench-Planning54.236.236.439.258.7RoboSpatial-Home55.745.363.262.361.8ShareRobot-Aff.26.819.825.525.59.0ShareRobot-Traj.73.341.662.281.450.6Ego-Plan245.535.538.852.639.9

https://huggingface.co/tencent/HY-Embodied-0.5#spatial-understandingSpatial Understanding

BenchmarkHY-Embodied 0.5 MoT-2BQwen3-VL 2BQwen3-VL 4BRoboBrain 2.5 4BMiMo-Embodied 7B3DSRBench57.039.943.944.842.0All-Angles Bench55.142.346.743.849.0MindCube66.328.431.026.936.2MMSI-Bench33.223.625.120.531.9RefSpatial-Bench45.828.945.356.048.0SAT76.745.356.751.378.7SIBench-mini58.242.050.947.353.1SITE-Bench-Image62.752.361.057.949.9SITE-Bench-Video63.552.258.054.858.9ViewSpatial53.137.241.636.636.1VSIBench60.548.055.241.748.5Where2Place68.045.059.065.063.6 Note: Results for HY-Embodied-0.5 MoT-2B are reported in thinking mode, while for all other models, we report the better performance between non-thinking and thinking modes.

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%93%9A-citation📚 Citation

If you find it useful for your research and applications, please cite our paper using this BibTeX:

@article{tencent2026hyembodied05,
    title={HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents},
    author={Tencent Robotics X and HY Vision Team},
    journal={arXiv preprint arXiv:2604.07430},
    year={2026}
}

https://huggingface.co/tencent/HY-Embodied-0.5#%F0%9F%99%8F-acknowledgements🙏 Acknowledgements

We thank the Hugging Face community for their support and the open-source contributions that made this implementation possible.